June 2016
Intermediate to advanced
286 pages
8h 9m
English
Content preview from Data Analytics with Hadoop
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Part II. Workflows and Tools for Big Data Science
The second part of Data Analytics with Hadoop explores higher-level tools and workflows for practicing data scientists. Although a foundational knowledge of Hadoop, MapReduce, and Spark is required to understand what kind of analyses can be conducted at scale, the day-to-day efforts of a data scientist engaging in big data will generally revolve around the ecosystem of tools built on top of Hadoop. Generally speaking, we have organized these final chapters around the data product pipeline presented in Chapter 1.
Chapter 6 discusses data warehousing and data mining and introduces Hive and HBase for both relational and columnar data storage and queries. Chapter 7 identifies the need for ingestion utilities to get data into HDFS and looks at structured methods using Sqoop, as well as less structured ingestion using Flume. Chapter 8 explores higher-level APIs for analytics: Apache Pig and Spark DataFrames. Chapter 9 discusses machine learning and computational methods using Spark MLlib. Finally, Chapter 10 wraps things up and takes a complete view of doing data science by summarizing the integration of the workflows discussed in the previous chapters of this part.
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.